Purpose

Using the target and gene clusters previously computed, we cluster on the cluster averages. The hierarchical clustering is used to reorder the heatmaps.

Initialization

Libraries

library(magrittr)
library(tidyverse)
library(pheatmap)
library(bluster)
library(Matrix)

Parameters

set.seed(20210818)

CLUSTERS_GENE_UNREG=c(9,12,13)

## input files
FILE_CLUSTERS_TARGETS="tbl/df_target_nnclusters_kd6_essential_ui10.csv"
FILE_CLUSTERS_GENES="tbl/df_gene_nnclusters_kd6_essential_ui10.csv"
RDS_DWUI="data/dwui/20221107-dwui-target-gene-kd6-essential.Rds"
RDS_ZDWUI="data/dwui/20221107-zdwui-target-gene-kd6-essential.Rds"

## output files
FILE_OUT_AVG_TARGET="img/heatmap-kd6-essential-avg-target-clusters.pdf"
FILE_OUT_AVG_GENE="img/heatmap-kd6-essential-avg-gene-clusters.pdf"
FILE_OUT_AVG_BOTH="img/heatmap-kd6-essential-avg-both-clusters.pdf"
FILE_OUT_FINAL="img/heatmap-kd6-essential-nnclusters-ordered.pdf"

## aesthetics
NCOLORS=100
COLORS_BWR <- colorRampPalette(c("blue", "white", "red"))(NCOLORS)
COLORS_MKY <- colorRampPalette(c("magenta", "black", "yellow"))(NCOLORS)
COLORS_YKM <- colorRampPalette(c("yellow", "black", "magenta"))(NCOLORS)
COLORS_RKG <- colorRampPalette(c("red", "black", "green"))(NCOLORS)
breaks_dwui <- seq(-0.5, 0.5, length.out=NCOLORS + 1)
breaks_zdwui <- seq(-4, 4, length.out=NCOLORS + 1)
breaks_zdwui_broad <- seq(-6, 6, length.out=NCOLORS + 1)
breaks_pca <- seq(-20, 20, length.out=NCOLORS + 1)

Data

Loading

dwui_target_gene <- readRDS("data/dwui/20221107-dwui-target-gene-kd6-essential.Rds")
dwui_target_gene[is.na(dwui_target_gene)] <- 0
zdwui_target_gene <- readRDS("data/dwui/20221107-zdwui-target-gene-kd6-essential.Rds")
zdwui_target_gene[is.na(zdwui_target_gene)] <- 0

df_cluster_target <- read_csv(FILE_CLUSTERS_TARGETS) %>% 
    mutate(cluster_id=factor(cluster_id, levels=sort(unique(cluster_id))))
df_cluster_gene <- read_csv(FILE_CLUSTERS_GENES) %>% 
    mutate(cluster_id=factor(cluster_id, levels=sort(unique(cluster_id))))

Preprocessing

sgid2gene <- df_cluster_target %>%
    dplyr::select(sgID_AB, target_gene) %>%
    distinct(sgID_AB, target_gene) %>%
    deframe()

ens2gene <- df_cluster_gene %>%
    dplyr::select(gene_id, gene_name) %>%
    distinct(gene_id, gene_name) %>%
    deframe()

convert_rownames <- function (mat, in2out) {
    mat %>% set_rownames(in2out[rownames(.)])
}

convert_colnames <- function (mat, in2out) {
    mat %>% set_colnames(in2out[colnames(.)])
}

idx_targets1 <- df_cluster_target %>%
    arrange(cluster_id, target_gene) %$%
    sgID_AB

idx_genes1 <- df_cluster_gene %>%
    arrange(cluster_id, gene_name) %$%
    gene_id

idx_genes_clean <- df_cluster_gene %>%
    filter(!(cluster_id %in% CLUSTERS_GENE_UNREG)) %>%
    arrange(cluster_id, gene_name) %$%
    gene_id

Compute Cluster Means

Target Clusters

I_cluster_target <- df_cluster_target %>%
    dplyr::select(sgID_AB, cluster_id) %>%
    deframe %>% fac2sparse

## cluster target means
M_cluster_target <- I_cluster_target %>% { . / rowSums(.) }

mzdwui_cluster_gene <- M_cluster_target[,idx_targets1] %*% zdwui_target_gene[idx_targets1,]
mzdwui_cluster_gene %<>% as.matrix

Plot Target Cluster Means

df_col_annots <- df_cluster_gene %>%
    dplyr::select(gene_id, cluster_id) %>%
    rename(gene_cluster=cluster_id) %>%
    column_to_rownames("gene_id")

pheatmap(mzdwui_cluster_gene[, idx_genes_clean], 
         color=COLORS_BWR,
         breaks=breaks_zdwui,
         fontsize_col=1, fontsize_row=10,
         annotation_col=df_col_annots,
         show_colnames=FALSE, show_rownames=TRUE, scale='none', 
         cluster_rows=TRUE, cluster_cols=FALSE, clustering_method="complete",
         cutree_rows=NA, cutree_cols=NA)

Reordered Target Cluster Means

idx_clusters_targets <- c(14,18,11,16,13,9,12,2,5,15,1,6,8,3,7,4,10,17)

pheatmap(mzdwui_cluster_gene[idx_clusters_targets, idx_genes_clean], 
         color=COLORS_BWR,
         breaks=breaks_zdwui,
         fontsize_col=1, fontsize_row=10,
         #annotation_row=df_row_annots, 
         annotation_col=df_col_annots,
         show_colnames=FALSE, show_rownames=TRUE, scale='none', 
         cluster_rows=FALSE, cluster_cols=FALSE, clustering_method="complete",
         #gaps_row=cumsum(table(clusters_targets_final)),
         #gaps_col=cumsum(table(clusters_genes_final)) %>% `[`(!duplicated(.)),
         cutree_rows=NA, cutree_cols=NA)

Reordered Target Clusters DWUI

idx_targets2 <- df_cluster_target %>%
    mutate(cluster_id=factor(cluster_id, levels=idx_clusters_targets)) %>%
    arrange(cluster_id, target_gene) %$%
    sgID_AB

df_row_annots <- df_cluster_target %>%
    dplyr::select(sgID_AB, cluster_id) %>%
    mutate(cluster_id=factor(cluster_id, levels=idx_clusters_targets)) %>%
    rename(target_cluster=cluster_id) %>%
    column_to_rownames("sgID_AB")

pheatmap(zdwui_target_gene[idx_targets2, idx_genes_clean], 
         color=COLORS_BWR,
         breaks=breaks_zdwui,
         fontsize_col=1, fontsize_row=10,
         annotation_row=df_row_annots, 
         annotation_col=df_col_annots,
         show_colnames=FALSE, show_rownames=FALSE, scale='none',
         cluster_rows=FALSE, cluster_cols=FALSE, clustering_method="complete")

Gene Clusters

I_cluster_gene <- df_cluster_gene %>%
    filter(!(cluster_id %in% CLUSTERS_GENE_UNREG)) %>%
    dplyr::select(gene_id, cluster_id) %>%
    deframe %>% fac2sparse

## cluster target means
M_cluster_gene <- I_cluster_gene %>% { . / rowSums(.) }

mzdwui_target_cluster <- zdwui_target_gene[,idx_genes_clean] %*% t(M_cluster_gene[,idx_genes_clean])
mzdwui_target_cluster %<>% as.matrix

Plot Gene Cluster Means

pheatmap(mzdwui_target_cluster[idx_targets2,], 
         color=COLORS_BWR,
         breaks=breaks_zdwui,
         fontsize_col=10, fontsize_row=10,
         show_colnames=TRUE, show_rownames=FALSE, scale='none', 
         angle_col=0, annotation_names_row=FALSE,
         annotation_row=df_row_annots, 
         cluster_rows=FALSE, cluster_cols=TRUE, clustering_method="complete", 
         drop_levels=TRUE)

Reordered Gene Cluster Means

idx_clusters_genes <- as.character(c(20,16,7,8,1,3,18,2,5,10,15,19,14,11,4,6,17))

pheatmap(mzdwui_target_cluster[idx_targets2, idx_clusters_genes], 
         color=COLORS_BWR,
         breaks=breaks_zdwui,
         fontsize_col=10, fontsize_row=10,
         show_colnames=TRUE, show_rownames=FALSE, scale='none', 
         annotation_row=df_row_annots, 
         cluster_rows=FALSE, cluster_cols=FALSE, clustering_method="complete", 
         drop_levels=TRUE)

Reordered All Clusters DWUI

idx_genes2 <- df_cluster_gene %>%
    filter(!(cluster_id %in% CLUSTERS_GENE_UNREG)) %>%
    mutate(cluster_id=factor(cluster_id, levels=idx_clusters_genes)) %>%
    arrange(cluster_id, gene_name) %$%
    gene_id

df_col_annots <- df_cluster_gene %>%
    dplyr::select(gene_id, cluster_id) %>%
    mutate(cluster_id=factor(cluster_id, levels=idx_clusters_genes)) %>%
    rename(gene_cluster=cluster_id) %>%
    column_to_rownames("gene_id")

gaps_col <- df_cluster_gene %>%
    filter(!(cluster_id %in% CLUSTERS_GENE_UNREG)) %>%
    mutate(cluster_id=factor(cluster_id, levels=idx_clusters_genes)) %>%
    arrange(cluster_id, gene_name) %$%
    table(cluster_id) %>%
    cumsum

gaps_row <- df_cluster_target %>%
    mutate(cluster_id=factor(cluster_id, levels=idx_clusters_targets)) %>%
    arrange(cluster_id, target_gene) %$%
    table(cluster_id) %>%
    cumsum
         
pheatmap(zdwui_target_gene[idx_targets2, idx_genes2], 
         color=COLORS_BWR,
         breaks=breaks_zdwui,
         fontsize_col=1, fontsize_row=10,
         annotation_row=df_row_annots, 
         annotation_col=df_col_annots,
         show_colnames=FALSE, show_rownames=FALSE, scale='none',
         annotation_names_row=FALSE, annotation_names_col=FALSE,
         gaps_row=gaps_row,
         gaps_col=gaps_col,
         cluster_rows=FALSE, cluster_cols=FALSE, clustering_method="complete")

Final Joint Plot

pheatmap(zdwui_target_gene[idx_targets2, idx_genes2], 
         color=COLORS_BWR,
         breaks=breaks_zdwui,
         fontsize_col=1, fontsize_row=1,
         annotation_row=df_row_annots, 
         annotation_col=df_col_annots,
         show_colnames=TRUE, show_rownames=TRUE, scale='none',
         labels_row=sgid2gene[idx_targets2],
         labels_col=ens2gene[idx_genes2],
         annotation_names_row=FALSE, annotation_names_col=FALSE,
         gaps_row=gaps_row,
         gaps_col=gaps_col,
         cluster_rows=FALSE, cluster_cols=FALSE,
         filename=FILE_OUT_FINAL, width=16, height=16)

Export Clustered Targets

pheatmap(mzdwui_cluster_gene[, idx_genes2], 
         color=COLORS_BWR,
         breaks=breaks_zdwui,
         fontsize_col=1, fontsize_row=10,
         annotation_col=df_col_annots,
         annotation_names_row=FALSE, annotation_names_col=FALSE,
         show_colnames=FALSE, show_rownames=TRUE, scale='none', 
         cluster_rows=TRUE, cluster_cols=FALSE, clustering_method="complete",
         cutree_rows=NA, cutree_cols=NA, 
         filename=FILE_OUT_AVG_TARGET, width=8, height=4)

Export Clustered Genes

pheatmap(mzdwui_target_cluster[idx_targets2,], 
         color=COLORS_BWR,
         breaks=breaks_zdwui,
         fontsize_col=10, fontsize_row=10,
         show_colnames=TRUE, show_rownames=FALSE, scale='none', 
         angle_col=0,
         annotation_row=df_row_annots, 
         annotation_names_row=FALSE, annotation_names_col=FALSE,
         cluster_rows=FALSE, cluster_cols=TRUE, clustering_method="complete", 
         drop_levels=TRUE, 
         filename=FILE_OUT_AVG_GENE, width=5, height=6)

Double Average

mzdwui_cluster_cluster <- M_cluster_target[,idx_targets2] %*% mzdwui_target_cluster[idx_targets2,]

pheatmap(mzdwui_cluster_cluster, 
         color=COLORS_BWR,
         breaks=breaks_zdwui,
         fontsize_col=10, fontsize_row=10,
         show_colnames=TRUE, show_rownames=TRUE, scale='none', 
         angle_col=0,
         cluster_rows=TRUE, cluster_cols=TRUE, clustering_method="complete")

Export Double Average

pheatmap(mzdwui_cluster_cluster, 
         color=COLORS_BWR,
         breaks=breaks_zdwui,
         fontsize_col=10, fontsize_row=10,
         show_colnames=TRUE, show_rownames=TRUE, scale='none', 
         angle_col=0,
         cluster_rows=TRUE, cluster_cols=TRUE, clustering_method="complete",
         filename=FILE_OUT_AVG_BOTH, width=5, height=4)

Runtime Details

Session Info

## R version 4.1.1 (2021-08-10)
## Platform: x86_64-apple-darwin13.4.0 (64-bit)
## Running under: macOS Big Sur 10.16
## 
## Matrix products: default
## BLAS/LAPACK: /Users/mfansler/miniconda3/envs/bioc_3_14/lib/libopenblasp-r0.3.18.dylib
## 
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
##  [1] Matrix_1.3-4    bluster_1.4.0   pheatmap_1.0.12 forcats_0.5.1  
##  [5] stringr_1.4.0   dplyr_1.0.8     purrr_0.3.4     readr_2.1.1    
##  [9] tidyr_1.1.4     tibble_3.1.7    ggplot2_3.3.5   tidyverse_1.3.1
## [13] magrittr_2.0.3 
## 
## loaded via a namespace (and not attached):
##  [1] httr_1.4.2           sass_0.4.0           bit64_4.0.5         
##  [4] vroom_1.5.7          jsonlite_1.7.2       modelr_0.1.8        
##  [7] bslib_0.3.1          assertthat_0.2.1     highr_0.9           
## [10] stats4_4.1.1         cellranger_1.1.0     yaml_2.2.1          
## [13] pillar_1.7.0         backports_1.4.0      lattice_0.20-45     
## [16] glue_1.6.2           digest_0.6.29        RColorBrewer_1.1-2  
## [19] rvest_1.0.2          colorspace_2.0-2     htmltools_0.5.2     
## [22] pkgconfig_2.0.3      broom_0.8.0          haven_2.4.3         
## [25] scales_1.1.1         tzdb_0.2.0           BiocParallel_1.28.0 
## [28] generics_0.1.1       farver_2.1.0         ellipsis_0.3.2      
## [31] withr_2.4.3          BiocGenerics_0.40.0  cli_3.3.0           
## [34] crayon_1.4.2         readxl_1.3.1         evaluate_0.15       
## [37] fs_1.5.2             fansi_0.5.0          xml2_1.3.3          
## [40] tools_4.1.1          hms_1.1.1            lifecycle_1.0.1     
## [43] S4Vectors_0.32.0     munsell_0.5.0        reprex_2.0.1        
## [46] cluster_2.1.2        compiler_4.1.1       jquerylib_0.1.4     
## [49] rlang_1.0.2          grid_4.1.1           BiocNeighbors_1.12.0
## [52] rstudioapi_0.13      igraph_1.2.9         rmarkdown_2.11      
## [55] gtable_0.3.0         DBI_1.1.1            R6_2.5.1            
## [58] lubridate_1.8.0      knitr_1.39           fastmap_1.1.0       
## [61] bit_4.0.4            utf8_1.2.2           stringi_1.7.6       
## [64] parallel_4.1.1       Rcpp_1.0.7           vctrs_0.4.1         
## [67] dbplyr_2.1.1         tidyselect_1.1.1     xfun_0.30

Conda Environment

## Conda Environment YAML
name: base
channels:
  - conda-forge
  - bioconda
  - defaults
dependencies:
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  - nbformat=5.1.3=pyhd8ed1ab_0
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  - nettle=3.6=hedd7734_0
  - oauthlib=3.1.1=pyhd8ed1ab_0
  - openssl=1.1.1s=hfd90126_0
  - pango=1.48.9=ha05cd14_0
  - patch=2.7.6=hbcf498f_1002
  - pcre=8.45=he49afe7_0
  - pcre2=10.37=ha16e1b2_0
  - perl=5.32.1=0_h0d85af4_perl5
  - pigz=2.6=h5dbffcc_0
  - pip=21.2.4=pyhd8ed1ab_0
  - pixman=0.40.0=hbcb3906_0
  - pkginfo=1.7.1=pyhd8ed1ab_0
  - popt=1.16=h7b079dc_2002
  - prompt-toolkit=3.0.20=pyha770c72_0
  - prompt_toolkit=3.0.20=hd8ed1ab_0
  - psutil=5.9.2=py39ha30fb19_0
  - py-lief=0.11.5=py39h9fcab8e_0
  - pyasn1=0.4.8=py_0
  - pybind11-abi=4=hd8ed1ab_3
  - pycosat=0.6.3=py39h63b48b0_1010
  - pycparser=2.20=pyh9f0ad1d_2
  - pycrypto=2.6.1=py39h89e85a6_1006
  - pygithub=1.53=py_0
  - pygments=2.10.0=pyhd8ed1ab_0
  - pyjwt=1.7.1=py_0
  - pyrsistent=0.18.1=py39h63b48b0_1
  - pysocks=1.7.1=pyha2e5f31_6
  - python=3.9.13=h57e37ff_0_cpython
  - python-dateutil=2.8.2=pyhd8ed1ab_0
  - python-libarchive-c=4.0=py39h6e9494a_1
  - python-tzdata=2021.5=pyhd8ed1ab_0
  - python_abi=3.9=2_cp39
  - pytz=2021.1=pyhd8ed1ab_0
  - pytz-deprecation-shim=0.1.0.post0=py39h6e9494a_2
  - pyyaml=5.4.1=py39h701faf5_3
  - readline=8.1.2=h3899abd_0
  - reproc=14.2.3=h0d85af4_0
  - reproc-cpp=14.2.3=he49afe7_0
  - requests=2.28.1=pyhd8ed1ab_1
  - requests-oauthlib=1.3.0=pyh9f0ad1d_0
  - rich=10.16.1=pyhd8ed1ab_0
  - ripgrep=13.0.0=h244e342_0
  - rsa=4.7.2=pyh44b312d_0
  - rsync=3.2.7=ha1fed10_0
  - ruamel.yaml=0.17.21=py39h63b48b0_1
  - ruamel.yaml.clib=0.2.6=py39h63b48b0_1
  - ruamel_yaml=0.15.80=py39h701faf5_1007
  - s3transfer=0.6.0=pyhd8ed1ab_0
  - scrypt=0.8.18=py39hbfd427f_4
  - setuptools=65.3.0=pyhd8ed1ab_1
  - six=1.16.0=pyh6c4a22f_0
  - smartmontools=7.2=h940c156_0
  - smmap=3.0.5=pyh44b312d_0
  - soupsieve=2.3.1=pyhd8ed1ab_0
  - sqlite=3.38.5=hd9f0692_0
  - tapi=1100.0.11=h9ce4665_0
  - tk=8.6.12=h5dbffcc_0
  - toolz=0.11.1=py_0
  - tornado=6.2=py39h701faf5_0
  - tqdm=4.62.2=pyhd8ed1ab_0
  - traitlets=5.1.0=pyhd8ed1ab_0
  - typing_extensions=3.10.0.0=pyha770c72_0
  - tzdata=2021e=he74cb21_0
  - tzlocal=4.2=py39h6e9494a_1
  - urllib3=1.26.6=pyhd8ed1ab_0
  - vsts-python-api=0.1.22=py_0
  - watchgod=0.7=pyhd8ed1ab_0
  - wcwidth=0.2.5=pyh9f0ad1d_2
  - wget=1.20.3=h52ee1ee_1
  - wheel=0.37.0=pyhd8ed1ab_1
  - wrapt=1.14.1=py39h701faf5_0
  - xxhash=0.8.0=h35c211d_3
  - xz=5.2.5=haf1e3a3_1
  - yaml=0.2.5=haf1e3a3_0
  - yaml-cpp=0.7.0=hb486fe8_1
  - zipp=3.5.0=pyhd8ed1ab_0
  - zlib=1.2.13=hfd90126_4
  - zstd=1.5.2=hfa58983_4
  - pip:
    - pyopenssl==20.0.1
prefix: /Users/mfansler/miniconda3